Data Manager

SHIVAM SINHA

*** ***** ** ******** ** +1-510-***-**** ************@***.********.***

EDUCATION

University of California, Berkeley- Haas School of Business 2016 Master of Financial Engineering (MFE)

Indian Institute of Technology (IIT), Varanasi 2006 Bachelor of Technology

Publications: Four research publications on neural network programming in acclaimed journals Series 3, FRM (Charter holder), CFA Level 1

SKILLS SUMMARY

Data Science: Experience in quantitative research, machine learning, artificial intelligence, statistical modeling, time series analysis and optimization methods

Programming: Python (numpy, pandas, matplotlib & scikit-learn), Matlab, SQL, VBA and Excel EXPERIENCE

Trexquant Investment, Stamford – Quant Researcher 07/18 to Present

Research & back test systematic strategies on cash equity and futures - VIX, treasury, FX and commodity futures

Performed transaction cost analysis using implementation shortfall and parametric models

Developed robust variance-covariance matrix by using regularization methods – factor models and shrinkage

Performed statistical analysis and developed models using machine learning techniques such as neural networks, clustering, random forest, logistic regression and LASSO

Robust estimation of regression models by considering estimation errors - Heteroskedasticity, Auto- correlation and multi-colinearity

Used regularized regression, clustering models, neural networks and PCA to analyze data and build signals

Cleaned data by removing outliers and filling missing data and extracted meaningful information

Extracted financial statement data from SEC Edgar files using Python and built trading strategies Pacific Life, New York – Quant Researcher 05/16 to 06/18

Developed and maintained trading models on multi-asset class - equity, treasury, FX and commodity futures

Provided quant support to PMs in analyzing new investment ideas and determining portfolio risks

Performed liquidity analysis using market microstructure to determine execution time and capacity of intraday trading strategies

Developed predictive models using artificial intelligence, logistic and regularized regression

Selected factors for regression models using LASSO and reduced over fitting by using Ridge regression

Used optimization methods – mean-variance, Black Litterman and risk parity to rebalance portfolios

Used Nanex tick data tapes to build data variables by removing outliers and filling missing data

Developed models to capture non linearity in data and used ensemble methods for robust prediction

Reviewed academic literature and performed empirical analysis to validate the results presented in papers. Olam International, Singapore -Associate 2/13 to 3/15

Developed momentum and volatility based trading signals on FX and commodities futures and options

Monitored options Greeks and performed stress testing and provided quantitative support to traders

Performed data cleaning of time series data by removing outliers and filling missing values

Used hedging strategies such as no-cost collar and call/put spreads to mitigate price and currency risks Goldman Sachs (GSAM), India- Associate, Asset Management 11/10 to 8/12

Created equity and FI model portfolios and advised portfolio managers about appropriate benchmarks

Calculated portfolio risk and P&L in “What-if” situations – such as weakening/strengthening of spreads

(CDS/Z Spread) and non-parallel shifts in yield curves

Performed mean-variance analysis to calculate optimal portfolio weights, analyzed Portfolio attribution reports and rebalanced portfolios to provide FX and duration hedges Evalueserve, India - Manager, Investment Research 05/08 to 11/10

Developed and rebalanced bond indices managed by Deutsche Bank’s Index Quant team

Developed CDS valuation models and credit risk models – structural and reduced form models INDUSTRY PROJECTS

MFE Project, UC Berkeley 01/16 to 03/16

Macro Regime determination for factor investing. Developed Risk on/off, Interest rates and recession regimes to time risk premia factors.

The portfolio constructed based on macro regimes produced a gross Sharpe of 1.8 from 2000 to 2015 Mellon Capital Management, San Francisco 06/15 to 09/15

Determination of US dollar regime to decide optimal FX hedge ratio for international equity portfolios, the new methodology provided better returns and Sharpe ratio than 100% hedged and un-hedged portfolios. Dymon Asia – Macro Hedge fund, Singapore 12/14 to 2/15

Analyzed DTCC data to find “Magnet” strikes for currencies in high and low volatility environments.

Developed a model to determine one-month Indicative volatility for currency options using volatility surface. ADDITIONAL INFORMATION

Scholarships: Sponsorship from Swiss Nuclear Safety (HSK) to do a research project at ETH-Zurich, 2006, Indian Academy of Science (IAS) Fellowship, 2005 and JNSACR Fellowship, 2004

Represented IIT Varanasi in National Inter-University Squash tournament, 2006

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